A new approach to integrating data from multiple informants in psychiatric assessment and research: mixing and matching contexts and perspectives

Abstract

Objective: When there exists no single source of information (informant) to validly measure a characteristic, it is typically recommended that data from multiple informants be used. In psychiatric assessment and research, however, multiple informants often provide discordant data, which further confuse the measurement. Strategies such as arbitrarily choosing one informant or using the data from all informants separately generate further problems. This report proposes a theory to explain observed patterns of interinformant discordance and suggests a new approach to using data from multiple informants to measure characteristics of interest.

Method: Using the example of assessment of developmental psychopathology in children, the authors propose a model in which the choice of informants is based on conceptualizing the contexts and perspectives that influence expression of the characteristic of interest and then identifying informants who represent those contexts and perspectives in such a way as to have the weaknesses of one informant canceled by the strengths of another.

Results: Applications of this approach to several datasets indicate that when these principles are followed, a more reliable and valid consensus measure is obtained, and failure to obtain a reliable, valid measure is indicative of some deviation from the principles.

Conclusions: In obtaining a consensus measure, the issue is not determining how many informants are needed but choosing the right set of informants.